Give Your Business Competitive Edge with Data Cleansing
Data cleansing is the process of identifying and correcting errors and discrepancies in a set of data to improve its quality. Data cleansing services not just aim at cleaning the data, but also brings uniformity to different sets of data so that it can accessed as and when required. Data cleansing services involve rebuilding, merging, migration, rebuilding, de-duplication, standardization, normalization, enriching, verifying & appending missing data. In other words, it includes everything that it takes to provide clean and consistent data.

If the data is not updated regularly and has discrepancies in it with duplicates, it causes errors in analysis, reporting and also affects the business decisions. Data cleansing solutions increase the ROI and productivity of your company while reducing the wastage and cost. It is best suited for your company to outsource data cleansing services as it can benefit you in many ways- by saving ample time which can be used for other productive and more important tasks, by reducing operation costs significantly, etc.

Why is Data Cleansing Needed?

All the important information about the customers is needed to be kept in a well-organized manner so that it can be retrieved easily anytime and anywhere. Sending online and offline campaigns to inaccurate data can waste a lot of time and money. It has become a matter of concern for almost every organization today to properly and accurately maintain its data. It turns out to be a great disadvantage for companies when targeting prospective customers. This is where the need for Data Cleansing Services arise. Data Cleansing is critical to maximize the value of your customers and prospects.

The Process of Data Cleansing:

Importing of data: Unorganized data in an Excel, CSV, or Tab-Separated Text file format is imported from your systems. 

Merging the data sets: Data present in different formats such as Excel, CSV, SQL, salesforce etc., is converted and merged to create a common database.

Rebuilding missing data: Missing information like post codes, state, country, phone area codes, gender, web address from email addresses etc., is added wherever needed.

Standardizing the data:  To have the same type of information in each column, the data is combined, separated or modified. 

Normalizing: Uniformity is maintained by checking and reformatting where necessary. Similar data like mister, Mr., mr are all converted to Mr. 

De-Duplicating the data: Duplicate copies of repeating data is eliminated and the database is updated accordingly.

Verifying and enriching: Data is validated against internal and external database sources and additional value-adding info is appended.  

Exporting the data: Clean and accurate CRM data is then exported in the desired format.

What Are the Benefits Of Data Cleansing?

Accurate and clean data is vital for effective sales, marketing and client management strategies. 

Data cleansing offers a range of benefits which improve company’s profitability:

• Richer analytics, improved market segmentation, higher response rates and reduced risk. 
• Makes sure that the right customer gets the right information.
• With the help of deduplication, costs associated with multiple mailings to the same contact reduces.
• Efforts and costs involved in marketing to contacts with incorrect details gets eliminated.
• Accurate contact details help in improving response rates.
• Intended audience can be reached effectively and quickly with valid emails. 

For More Information

SunTec Data, a leading provider of data cleansing services, has the experience of servicing many global clients as per their needs. So, what’s the wait about? Outsource your data cleansing services to SunTec Data, who provides cost-effective services without compromising on quality. 
Drop a mail at info@suntecdata.com to discuss your project.
Give Your Business Competitive Edge with Data Cleansing
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Give Your Business Competitive Edge with Data Cleansing

Data cleansing is the process of identifying and correcting errors and discrepancies in a set of data to improve its quality. Data cleansing serv Read More
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37
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Published: